Presentation | 1995/5/25 On Prediction Models of Community Response to Acoustical Environment (III) : Predicting with Neural Network from Leq's for Several Time-Sections Shigeki Kawai, Yuichi Noro, Kazuhiro Kuno, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This report presents two prediction models of community response to environmental noise. These models predict the distribution of inhabitants' reactions using neural network from equivalent sound level for 24hours (Leq24) or Leq's for four time-sections in a day (LeqM, LeqD, LeqE, LeqN). We applied them to the social survey data for acoustical environment around residence in Nagoya city and discussed their accuracy and validity. Consequently, these models predicted the distribution of community responses as precisely as previous reported model which predicts community responses from categorized Leq24 and land use. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | community response / prediction / neural network / equivalent sound level |
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Committee | EA |
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Conference Date | 1995/5/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Engineering Acoustics (EA) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | On Prediction Models of Community Response to Acoustical Environment (III) : Predicting with Neural Network from Leq's for Several Time-Sections |
Sub Title (in English) | |
Keyword(1) | community response |
Keyword(2) | prediction |
Keyword(3) | neural network |
Keyword(4) | equivalent sound level |
1st Author's Name | Shigeki Kawai |
1st Author's Affiliation | Faculty of Engineering, Mie University() |
2nd Author's Name | Yuichi Noro |
2nd Author's Affiliation | Faculty of Engineering, Mie University |
3rd Author's Name | Kazuhiro Kuno |
3rd Author's Affiliation | Faculty of Engineering, Mie University |
Date | 1995/5/25 |
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Volume (vol) | vol.95 |
Number (no) | 69 |
Page | pp.pp.- |
#Pages | 7 |
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